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Google app market captures the school of thought of users via ratings and text reviews. The critique's viewpoint regarding an app is proportional to their satisfaction level. Consequently, this helps other users to gain insights before downloading or purchasing the apps. The potential information from the reviews can't be extracted manually, due to its exponential growth. Sentiment analysis, by machine learning algorithms employing NLP, is used to explicitly uncover and interpret the emotions. This study aims to perform the sentiment classification of the app reviews and identify the university students' behaviour towards the app market. We applied machine learning algorithms using TF-IDF text representation scheme and the performance was evaluated on ensemble learning method. Our model was trained on Google reviews and tested on students' reviews. SVM recorded the maximum accuracy(93.37%), Fscore(0.88) on tri-gram + TF-IDF scheme. Bagging enhanced the performance of LR and NB with accuracy of 87.80% and 85.5% respectively.

Google app market captures the school of thought of users from every corner of the globe via ratings and text reviews, in a multilinguistic arena. The critique's viewpoint regarding an app is proportional to their satisfaction level. The potential information from the reviews cannot be extracted manually, due to its exponential growth.So, sentiment analysis, by machine learning and deep learning algorithms employing NLP, explicitly uncovers and interprets the emotions. This study performs the sentiment classification of the app reviews and identifies the university students' behavior toward the app market via exploratory analysis. We applied machine learning algorithms using the TP, TF, and TF-IDF text representation scheme and evaluated its performance on Bagging, an ensemble learning method. We used word embedding, GloVe, on the deep learning paradigms. Our model was trained on Google app reviews and tested on students' app reviews (SAR). The various combinations of these algorithms were compared among each other using F-score and accuracy and inferences were highlighted graphically. SVM, among other classifiers, gave fruitful accuracy (93.41%), F-score (0.89) on bi-gram + TF-IDF scheme. Bagging enhanced the performance of LR and NB with accuracy 87.88% and 86.69% and F-score 0.86 and 0.78 respectively. Overall, LSTM on Glove embedding recorded the highest accuracy (95.2%) and F-score (0.88).

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